Outlier detection (OD), also known as anomaly detection, is a key machine learning task to identify the deviant samples from the general data distribution [1]}, [2]}.
With the increasing importance of graph data in both research and real-world applications [3]}, [4]}, [5]}, [6]}, [7]}, detecting outliers with graph-based methods such as graph neural networks (GNNs) recently have drawn much attention [8]}, [9]}, [10]} with many applications such as detecting suspicious activities in social networks [11]}, [12]} and security systems [13]}.
